Creating Roman Datamodels

There are a few options to create a new datamodel. Each has specific use cases and understanding what happens during each option is vital to selecting which to use.

DataModel.create_minimal

roman_datamodels.datamodels.DataModel.create_minimal inspects the datamodel schema and creates a model that contains values that are unambiguously defined in the schema. The resulting model will lack most required attributes (since most attributes can be more than one value) but will have the general structure expected by the schema.

This method is most useful for pipeline code when a new model is needed. For example, the source catalog step needs to produce a new roman_datamodels.datamodels.ImageSourceCatalogModel model with contents determined from the input roman_datamodels.datamodels.ImageModel. roman_datamodels.datamodels.DataModel.create_minimal can be used to construct a model which is then filled out with contents derived from the roman_datamodels.datamodels.ImageModel and then validated to confirm that it contains all the required values.

DataModel.create_fake_data

roman_datamodels.datamodels.DataModel.create_fake_data inspects the datamodel schema and creates a valid datamodel that contains “fake” data (for example all required strings will be set to “?”). This is dangerous to use within pipeline and user code as it greatly reduces the utility of the schema validation.

The main use case for this method is unit tests where often a small targeted operation is tested. In this case a valid model is often needed but most of the model contents are not of concern.

DataModel.__init__

DataModel.__init__ constructs a new datamodel which optionally contains the provided roman_datamodels.stnode.TaggedObjectNode. This is used when roman_datamodels.datamodels.open constructs a new datamodel from an ASDF file which contains the serialized contents of the roman_datamodels.stnode.TaggedObjectNode. However this is less useful for pipeline and/or test code where no node contents are available as the produced datamodel will be invalid since it lacks all required attributes.

Example

To compare the above options let’s consider a toy example WidgetModel with the following schema:

%YAML 1.1
---
$schema: asdf://stsci.edu/datamodels/roman/schemas/rad_schema-1.0.0
id: asdf://stsci.edu/datamodels/roman/schemas/reference_files/dark-1.0.0

title: Widget model schema

datamodel_name: WidgetModel

type: object
properties:
  meta:
    properties:
      telescope:
        description: "Name of the telescope"
        type: string
        enum: [ROMAN]
      target:
        description: "Target observed"
        type: string
    required: [telescope, target]
  data:
    tag: tag:stsci.edu:asdf/core/ndarray-1.*
    ndim: 3
    datatype: uint16
required: [meta, data]

This schemas requires that the model have:

• meta.telescope which must be “ROMAN”

• meta.target which can be any string

• data which can be any 3 dimensional array containing 16 bit unsigned integers

roman_datamodels.datamodels.DataModel.create_minimal will construct a WidgetModel with only meta.telescope defined (since it must always be “ROMAN”). Both meta.target and data will not be defined and attempting to save this model will produce a validation error (since both those attributes are required).

roman_datamodels.datamodels.DataModel.create_fake_data will produce a WidgetModel with all required attributes containing “fake” values:

• meta.telescope will be “ROMAN” (the only allowed value)

• meta.target will be “?” (the default fake string value)

• data will contain all zeros

As all the required attributes are defined this model is valid and can be saved.

Datamodel.__init__ will produce a WidgetModel with no attributes defined. Attempting to save this model will result in a validation error as it lacks all required attributes.